In fact, VGG13 is also an extension and extension of Alexnet. AlexNet has adjusted the convolutional network in depth.
Combat a CIRF100 today, the convolution process:
import tensorflow as tf
from tensorflow.keras import layers,optimizers,datasets,Sequential
import os
# os.environ['TF_CPP_MIN_LOG_LEVEL']='1' #显示所有信息
os.environ['TF_CPP_MIN_LOG_LEVEL']='1' #只显示Waring和Error信息
# os.environ['TF_CPP_MIN_LOG_LEVEL']='3' #只显示Error信息
tf.random.set_seed(2345)
#VGG的实质是AlexNet结构的增强版
#创建卷积层
conv_layers=[
#总共十层卷积网络
#第一组卷积和池化层
layers.Conv2D(64,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.Conv2D(64,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2,2],strides=2,padding="same"),
#第二组卷积和池化层
layers.Conv2D(128,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.Conv2D(128,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2,2],strides=2,padding="same"),
#第三组卷积和池化层
layers.Conv2D(256,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.Conv2D(256,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2,2],strides=2,padding="same"),
#第四组卷积和池化层
layers.Conv2D(512,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.Conv2D(512,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2,2],strides=2,padding="same"),
#第五组卷积和池化层
layers.Conv2D(512,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.Conv2D(512,kernel_size=[3,3],padding="same",activation=tf.nn.relu),
layers.MaxPool2D(pool_size=[2,2],strides=2,padding="same")
]
#数据预处理函数
def preprocess(x,y):
#[0-1]标准化
x = tf.cast(x,dtype=tf.float32) /255.
y = tf.cast(y,dtype=tf.int32)
return x,y
#加载数据集
(x,y),(x_test,y_test) = datasets.cifar100.load_data()
#挤压掉一个shape
y = tf.squeeze(y,axis=1)
y_test = tf.squeeze(y_test,axis=1)
print(x.shape,y.shape,x_test.shape,y_test.shape)
#
train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.shuffle(1000).map(preprocess).batch(64)
test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db = test_db.shuffle(1000).map(preprocess).batch(64)
def main():
# 创建卷积网络层,传入一个list
#输入[b,32,32,3] => [b,1,1,512]
conv_net=Sequential(conv_layers)
# conv_net.build(input_shape=[None, 32, 32, 3])
# #随便设定一个输入测试
# x = tf.random.normal([4,32,32,3])
# out = conv_net(x)
# print(out.shape)
#创建全连接层网络,三层网络
fc_net = Sequential([
layers.Dense(256,activation=tf.nn.relu),
layers.Dense(128,activation=tf.nn.relu),
layers.Dense(100,activation=None)
])
# 指定卷积输入
conv_net.build(input_shape=[None, 32, 32, 3])
#指定全连接输入
fc_net.build(input_shape=[None,512])
#设置优化器
optimizer = optimizers.Adam(lr = 1e-4)
#[1,2] +[3,4]=[1,2,3,4]
variables =conv_net.trainable_variables + fc_net.trainable_variables
#
for epoch in range(50):
for step,(x,y) in enumerate(train_db):
with tf.GradientTape() as tape:
# [b, 32, 32, 3] => [b, 1, 1, 512]
out =conv_net(x)
# flatten, => [b, 512]
#扁平化，搞成一列
out=tf.reshape(out,[-1,512])
# [b, 512] => [b, 100]
logits =fc_net(out)
#y一列输出，深度100# [b] => [b, 100]
y_onehot =tf.one_hot(y,depth=100)
#计算loss,交叉熵的方法
loss =tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True)
#计算均值
loss = tf.reduce_mean(loss)
grads = tape.gradient(loss,variables)
optimizer.apply_gradients(zip(grads,variables))
#每100次打印一下loss
if step % 100==0:
print(epoch,step,'loss:',float(loss))
total_num =0
total_correct =0
for x,y in test_db:
out =conv_net(x)
out = tf.reshapeout,[-1,512]
logits =fc_net(out)
prob =tf.nn.softmax(logits,axis=1)
pred = tf.argmax(prob,axis=1)
pred = tf.cast(pred,dtype=tf.int32)
correct =tf.cast(tf.equal(correct,y),dtype=tf.int32)
correct = tf.reduce_sum(correct)
total_num += x.shape[0]
total_correct += int(correct)
acc = total_correct / total_num
print(epoch,'acc:',acc)
if __name__ == "__main__":
main()Summarize:
Summary of the convolution process: 0. Data preprocessing (download data, adjust data format) input(10000, 32, 32, 3) output(10000,) 1. conv_layers creates a convolutional layer 2. fc_net creates a network layer 3. Convolution and network layers are connected Sequential() 4. Set the optimizer and loss function and iterate 5. Verify the accuracy of the data set
Before doing this, you need to know some basic knowledge:
import tensorflow as tf
t = [1,2,3,4,5,6,7,8,9]
n_t = tf.reshape(t,[3,3])
print(n_t)
# tf.Tensor(
# [[1 2 3]
# [4 5 6]
# [7 8 9]], shape=(3, 3), dtype=int32)
a1 = tf.random.normal([1,2,3,1])
print(a1)
# tf.Tensor(
# [[[[-0.61823624]
# [ 0.39007753]
# [-1.216511 ]]
#
# [[ 0.7147906 ]
# [ 0.04909178]
# [-0.9400272 ]]]], shape=(1, 2, 3, 1), dtype=float32)
#删除第一个维度
a2 = tf.squeeze(a1, axis=0)
print(a2.shape)
# (2, 3, 1)
a3 = tf.squeeze(a1)
print(a3.shape)
# (2, 3)
# 增加维度：expand_dims()
x = tf.random.normal([2,3])
y = tf.expand_dims(x, axis=0)
print(y.shape)
# (1, 2, 3)
#维度交换
a1 = tf.random.normal([10, 11, 12])
a2 = tf.transpose(a1, perm = [0, 2, 1])
print(a2.shape)
# (10, 12, 11)
items = [(1, 3), (2, 1), (3, 3),(4,5)]
#默认下表是0开始
for i, item in enumerate(items):
print(i,item)
# 0 (1, 3)
# 1 (2, 1)
# 2 (3, 3)
# 3 (4, 5)
#默认下表是从1开始
for i, item in enumerate(items, 1):
print(i, item)
# 输出：
# 1 (1, 3)
# 2 (2, 1)
# 3 (3, 3)
# 4 (4, 5)
names = ['1', '2', '3', '4']
jobs = ['a', 'b', 'c', 'd','f']# 长度可以不同
# 同时迭代多个列表
for name, job in zip(names, jobs):
print(name, job)
# 1 a
# 2 b
# 3 c
# 4 d
# 常用的是构建随机数据与连续序列数据：
# tf.random_normal 正态分布
# tf.random_uniform 均匀分布
# tf.random_npoisson 泊松分布
# tf.random_gamma 伽马分布
# tf.range 连续序列
# tf.zeros
# 特殊的张量可以使用下面函数来构建
# tf.Variable 函数用于创建变量//tf.global_variables_initializer()用于初始化所有变量；w.initializer用于初始化单个变量
# tf.constant 张量的创建
# tf.placeholder
# tf.SparseTensor
# tf.cast()修改张量数据的数据类型
# from keras.models import Model
# model = Model(ipt,opt) #定义模型，传入输入输出相应的层
# model.summary() #查看这个模型的网络层结构信息，如下图所示
# rmsprop = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
# model.compile(optimizer=rmsprop,loss='categorical_crossentropy',metrics=['acc'])
#
#
# 常见损失函数
#
# mean_squared_error：均方误差MSE
# mean_absolute_error：绝对值均差MAE
# binary_crossentropy：用于二分类
# categorical_crossentropy：用于多分类
#
#
# 常见的评价标准
# mae
# mse
# binary_accuracy
# categorical_accuracy